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AI with Market Research: Combining Next Generation Tech with Human Insights

AI with Market Research: Combining Next Generation Tech with Human Insights

Nothing is as difficult as the human brain, capable of carrying out multiple complex tasks at once. Yet technology is a catch-up, capable of automating procedures, speeding up project schedules, and so much more with the continuous improvement of machine learning and artificial intelligence technologies.

This concept is generally used to describe advanced technology that is not actually used to its full potential, or even technology that does not exist yet but will do very soon, when we discuss 'next generation technology'. Advanced robotics, artificial intelligence (and uniqueness), the Internet of Things, quantum computing, extended reality, blockchain, etc for example. To a certain degree, technology already affects global trade but still lacks maximum impact.

Fusing Technology and Human Insight

So, with next generation technology currently lacking the effect we need, let's see what it can do to close the gap by integrating next generation technology with human oversight.

Artificial intelligence progress is at an all-time high for all next-generation innovations, with more applications for business intelligence and data analytics being developed and sold as we speak. Under AI creativity, the fuel for this fire is necessity; through big data and an increasing need for fast feedback from consumers, we are looking at an ever-building data tsunami, far more than we could ever manage on our own. We need assistance, and that's why artificial intelligence is becoming a bigger part of the daily lives of researchers. And that's what makes artificial intelligence the ideal first example when addressing, for the purpose of future market research, the merger of next generation technology and human supervision.

The origins of artificial intelligence are already embedded in market research systems, in the incorporation of automation to create processes of data collection, visualization and analysis, so that insight practitioners can concentrate more on producing and activating insight.

But we are a long way from developing an artificial intelligence program that can do exactly what the human mind can do from participant sourcing to insight empowerment, in all phases of the research process, so we rely on machine learning algorithms that are filled with the prejudices we put there without a way to monitor their own actions. That's when it comes to human supervision. While we are biased, we can double check our own viewpoint and minimize any risks that these prejudices can pose, and we can do that for all next-generation innovations that make their way through market research systems, such as:

1.       Extended Reality (enables revolutionary experiences and is suitable for concept testing research and advanced visualization of data, but with human intuition, perception and analysis can only achieve its full potential).

2.       Internet of Things allows broader access to communication networks, thereby encouraging participants to have a wider variety of research channels, but it is up to human understanding to determine the channels to use for which research projects and which participants we want to reach).

3.       Voice assistants (although they may not yet be quite right in terms of information security, they may help us increase the usability of market research as they become safe communication networks - however we need human monitoring to help build the research tasks to ensure that they are right before they go out)

Balancing the Scales of Oversight

Let me ask you, when did you last hear the expression, "To err is human? It's generally said with an air of modesty when most people hear that word, asking someone else to forgive an error, as making a mistake is becoming a victim of inevitable human nature.

Human error is the primary cause of security breaches, incorrect analysis of results, inaccurate insights, and a host of other damning encounters that the industry of insights has had to wade through since its conception. Human error is the source of incorrect elections, airline crashes, cybersecurity problems, etc., but also technological breakthroughs in the planet, zooming out to take a broader look. While some errors produce true results, most of them have harmful effects that if we were more cautious, might have been prevented. To fail is human, but to err is to probably lose a company its life in an industry where errors have real-world consequences.

Automated processes of next-generation technologies, if we stick to the example of artificial intelligence and automation, are the most poignant example of people trying to make up for their mistakes and can help eliminate human error at all levels, but still produce a few errors when trying to correct them. We perform three crucial roles in this example:

1.       We need to train machines to perform the assignments we need.

2.       We need to clarify the implications of these tasks, the outcomes we want and the results that are counterintuitive or contentious.

3.       The responsible use of computers must be maintained at all costs.

We are however the ones that build the automated processes that are intended to supercharge our own capacities, which means that without human supervision, these automated processes would have errors that we need to correct before they are truly qualified to take over. We need to iron out the algorithmic prejudices, train the AI systems to communicate with people in the best way, and teach them how to work to the best of their ability, all so that we can perform to the best of our ability.

The primary advantage of integrating human monitoring with this technology of the next generation is that we can detect and correct any vulnerabilities that occur before they disrupt the research process and projects that depend on that technology. But we need to be aware that any error can not be detected by humans, and when one falls past that, oversight takes on an entirely different, disappointing sense. Working with these automated processes and any next-generation technology that is built into our operating systems would be the best way to create a cohesive and beneficial research process where we all get what we need the most reliable, meaningful and impactful insights, as well as efficient research processes that halve the amount of work required to generate those insights.

The Benefits to Others

But one thing I still haven't discussed in all this is the advantages of using this mixed approach to anyone outside the industry, and it's really pretty simple. Collaboration and the optimized processes resulting from this collaboration between humans and technology of the next generation especially between humans and artificial intelligence, are important to many. It enables us to evolve key processes while still gathering the data we need to make the right decisions responsibly.

We are able to generate insights faster for stakeholders through this partnership, generating insights for them to act in real time making educated, impactful decisions at the drop of a hat that leads them towards success.